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Predicting driving direction with weighted markov model

Mao, Bo, Cao, Jie, Wu, Zhiang, Huang, Guangyan and Li, Jingjun 2012, Predicting driving direction with weighted markov model, in ADMA 2012 : Advanced Data Mining and Applications; 8th International Conference, ADMA 2012 Nanjing, China, December 15-18, 2012, Proceedings, Springer, Berlin, Germany, pp. 407-418, doi: 10.1007/978-3-642-35527-1_34.

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Title Predicting driving direction with weighted markov model
Author(s) Mao, Bo
Cao, Jie
Wu, Zhiang
Huang, GuangyanORCID iD for Huang, Guangyan
Li, Jingjun
Conference name Advanced Data Mining and Applications. Conference (8th : 2012 : Nanjing, China)
Conference location Nanjing, China
Conference dates 15-18 Dec. 2012
Title of proceedings ADMA 2012 : Advanced Data Mining and Applications; 8th International Conference, ADMA 2012 Nanjing, China, December 15-18, 2012, Proceedings
Editor(s) Zhou, Shuigeng
Zhang, Songmao
Karypis, George
Publication date 2012
Series Lecture Notes in Artificial Intelligence 7713
Start page 407
End page 418
Total pages 12
Publisher Springer
Place of publication Berlin, Germany
Keyword(s) driving direction prediction
trajectory mining
weighted PageRank
Summary Driving direction prediction can be useful in different applications such as driver warning and route recommendation. In this paper, a framework is proposed to predict the driving direction based on weighted Markov model. First the city POI (Point of Interesting) map is generated from trajectory data using weighted PageRank algorithm. Then, a weighted Markov model is trained for the near term driving direction prediction based on the POI map and historical trajectories. The experimental results on real-world data set indicate that the proposed method can improve the original Markov prediction model by 10% at some circumstances and 5% overall.
ISBN 9783642355271
ISSN 0302-9743
Language eng
DOI 10.1007/978-3-642-35527-1_34
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2013, Springer
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Document type: Conference Paper
Collection: School of Information Technology
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